Biblio

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.

This study investigates the relationship between resilience of control systems to attacks and the information available to malicious attackers. Specifically, it is shown that control systems are guaranteed to be secure in an asymptotic manner by rendering reactions against potentially harmful actions covert. The behaviors of the attacker and the defender are analyzed through a repeated signaling game with an undisclosed belief under covert reactions. In the typical setting of signaling games, reactions conducted by the defender are supposed to be public information and the measurability enables the attacker to accurately trace transitions of the defender's belief on existence of a malicious attacker. In contrast, the belief in the game considered in this paper is undisclosed and hence common equilibrium concepts can no longer be employed for the analysis. To surmount this difficulty, a novel framework for decision of reasonable strategies of the players in the game is introduced. Based on the presented framework, it is revealed that any reasonable strategy chosen by a rational malicious attacker converges to the benign behavior as long as the reactions performed by the defender are unobservable to the attacker. The result provides an explicit relationship between resilience and information, which indicates the importance of covertness of reactions for designing secure control systems.

This paper studies the sensor placement problem in a networked control system for improving its security against cyber-physical attacks. The problem is formulated as a zero-sum game between an attacker and a detector. The attacker's decision is to select f nodes of the network to attack whereas the detector's decision is to place f sensors to detect the presence of the attack signals. In our formulation, the attacker minimizes its visibility, defined as the system L2 gain from the attack signals to the deployed sensors' outputs, and the detector maximizes the visibility of the attack signals. The equilibrium strategy of the game determines the optimal locations of the sensors. The existence of Nash equilibrium for the attacker-detector game is studied when the underlying connectivity graph is a directed or an undirected tree. When the game does not admit a Nash equilibrium, it is shown that the Stackelberg equilibrium of the game, with the detector as the game leader, can be computed efficiently. Our results show that, under the optimal sensor placement strategy, an undirected topology provides a higher security level for a networked control system compared with its corresponding directed topology.

In this talk, we discuss how control theory can contribute to the analysis and design of secure cyber-physical systems. We start by reviewing conditions for undetectable false-data injection attacks on feedback control systems. In particular, we highlight how a physical understanding of the controlled process can guide us in the allocation of protective measures. We show that protecting only a few carefully selected actuators or sensors can give indirect protection to many more components. We then illustrate how such analysis is exploited in the design of a resilient control scheme for a microgrid energy management system.

Industrial control systems are cyber-physical systems that are used to operate critical infrastructures such as smart grids, traffic systems, industrial facilities, and water distribution networks. The digitalization of these systems increases their efficiency and decreases their cost of operation, but also makes them more vulnerable to cyber-attacks. In order to protect industrial control systems from cyber-attacks, the installation of multiple layers of security measures is necessary. In this paper, we study how to allocate a large number of security measures under a limited budget, such as to minimize the total risk of cyber-attacks. The security measure allocation problem formulated in this way is a combinatorial optimization problem subject to a knapsack (budget) constraint. The formulated problem is NP-hard, therefore we propose a method to exploit submodularity of the objective function so that polynomial time algorithms can be applied to obtain solutions with guaranteed approximation bounds. The problem formulation requires a preprocessing step in which attack scenarios are selected, and impacts and likelihoods of these scenarios are estimated. We discuss how the proposed method can be applied in practice.

 

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.